6. Developing synergism with operational hydrology
Recent advances in hydrologic modeling and DA offer large potential for improving operational hydrologic forecasting and water resources management. Conversely, additional needs for such improvements present new opportunities and challenges for hydrologic modeling and DA. This sub-theme discusses the status and plans for the Hydrologic DA Testbed, an ongoing effort to promote such synergism, and presents the initial results.
|Chair:||Yuqiong Liu, NASA, University of Maryland|
|Keynote 1:||Soroosh Sorooshian, University of California, Irvine: “Challenges and Limitations of Hydroclimatological Forecasting and the Relative Role of Its Three Pillars: Models, Observations and Parameterization”|
|In response to society’s need for more effective tools to address hydrologic hazards and manage water resources systems, engineers and scientists have become more reliant on the use of predictive models and stochastic methods. Depending on the problems, the hydrometeorological information needed may range from hourly forecasts (i.e., in the case of flash floods) to seasonal to inter-annual (i.e., in the case of reservoir operation), and to decadal to century (i.e., in the case of long range water supply planning and structural designs). While there is a rich body of literature reporting on progress related to both, “weather-scale” and “climate-scale” hydrologic predictions, many challenges face the research community attempting to extend the lead time and accuracy of predictions.
More specifically, despite the progress in each of the three pillars of hydrometeorological prediction system (models, observations and parameterization) over the past several decades, the improvements in the overall forecast quality is yet to reach the users expectations. This presentation will provide a summary of both the progress and the related challenges. It will be a personal reflection of over 3 decades of research experience with hydrologic modeling and involvement with a number of international initiatives.
|Keynote 2:||Yuqiong Liu, NASA and The University of Maryland: “Toward Improving Operational Streamflow Forecasting at Regional and Continental Scales by Assimilating Satellite-Based Snow Observations Using the NASA Land Information System”|
|In snow-dominated river basins, snowpack represents a major uncertainty source for the forecast model. Hence, accurate analyses of snowpack conditions and its spatiotemporal variability are essential to producing reliable hydrologic forecasts at regional and continental scales. Satellite-based observations are increasingly being considered for assisting hydrologic forecasting in recent years due to their relatively short latency, increasing spatial resolution, and reasonable spatiotemporal continuity. A great deal of research has recently been conducted at NASA GSFC with regards to assimilating satellite-based products of snow depth and snow cover into the NASA Land Information System (LIS) for various hydrologic applications. In this presentation, we focus on the impacts of satellite-based snow data assimilation on streamflow forecasting in the Upper Colorado River Basin and across the contiguous US. Our results indicate that, with proper preprocessing to account for potential biases, satellite-based snow observations can have potential in improving streamflow predictions at various spatiotemporal scales.|
|Oral 1:||Dong-Jun Seo, The University of Texas at Arlington, USA: “High-resolution Flash Flood Forecasting for the Dallas-Fort Worth Metroplex (DFW)”|
|Oral 2:||Harrie-Jan Hendricks-Franssen, Agrosphere (IBG-3), Forschungszentrum Julich, Germany: “Real-time Control of Irrigation by Assimilating Measured Soil Moisture Contents into CLM: A Case Study in Spain”|
|Oral 3:||Ahmad Tavakoly, The University of Texas at Austin, USA: “Extension of Regional River Flow Modeling to the Continental Scale of the MississippiRiver Basin by Using High Resolution River Data from NHDPlus Dataset”|
|Oral 4:||Narendra N. Das, Jet Propulsion Laboratory (JPL), USA “SMAP High Resolution Data for Assimilation in Geophysical Applications”|